org.apache.spark.api.java
Interface JavaRDDLike<T,This extends JavaRDDLike<T,This>>

All Superinterfaces:

java.io.Serializable

All Known Implementing Classes:

JavaDoubleRDD, JavaHadoopRDD, JavaNewHadoopRDD, JavaPairRDD, JavaRDD

public interface JavaRDDLike<T,This extends JavaRDDLike<T,This>>
extends scala.Serializable

Defines operations common to several Java RDD implementations. Note that this trait is not intended to be implemented by user code.

Method Summary

<U> U	aggregate(U zeroValue, Function2<U,T,U> seqOp, Function2<U,U,U> combOp) Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral "zero value".
<U> JavaPairRDD<T,U>	cartesian(JavaRDDLike<U,?> other) Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of elements (a, b) where a is in this and b is in other.
void	checkpoint() Mark this RDD for checkpointing.
scala.reflect.ClassTag<T>	classTag()
java.util.List<T>	collect() Return an array that contains all of the elements in this RDD.
JavaFutureAction<java.util.List<T>>	collectAsync() The asynchronous version of collect, which returns a future for retrieving an array containing all of the elements in this RDD.
java.util.List<T>[]	collectPartitions(int[] partitionIds) Return an array that contains all of the elements in a specific partition of this RDD.
SparkContext	context() The SparkContext that this RDD was created on.
long	count() Return the number of elements in the RDD.
PartialResult<BoundedDouble>	countApprox(long timeout) :: Experimental :: Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
PartialResult<BoundedDouble>	countApprox(long timeout, double confidence) :: Experimental :: Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
long	countApproxDistinct(double relativeSD) Return approximate number of distinct elements in the RDD.
JavaFutureAction<Long>	countAsync() The asynchronous version of count, which returns a future for counting the number of elements in this RDD.
java.util.Map<T,Long>	countByValue() Return the count of each unique value in this RDD as a map of (value, count) pairs.
PartialResult<java.util.Map<T,BoundedDouble>>	countByValueApprox(long timeout) (Experimental) Approximate version of countByValue().
PartialResult<java.util.Map<T,BoundedDouble>>	countByValueApprox(long timeout, double confidence) (Experimental) Approximate version of countByValue().
T	first() Return the first element in this RDD.
<U> JavaRDD<U>	flatMap(FlatMapFunction<T,U> f) Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
JavaDoubleRDD	flatMapToDouble(DoubleFlatMapFunction<T> f) Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
<K2,V2> JavaPairRDD<K2,V2>	flatMapToPair(PairFlatMapFunction<T,K2,V2> f) Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
T	fold(T zeroValue, Function2<T,T,T> f) Aggregate the elements of each partition, and then the results for all the partitions, using a given associative and commutative function and a neutral "zero value".
void	foreach(VoidFunction<T> f) Applies a function f to all elements of this RDD.
JavaFutureAction<Void>	foreachAsync(VoidFunction<T> f) The asynchronous version of the foreach action, which applies a function f to all the elements of this RDD.
void	foreachPartition(VoidFunction<java.util.Iterator<T>> f) Applies a function f to each partition of this RDD.
JavaFutureAction<Void>	foreachPartitionAsync(VoidFunction<java.util.Iterator<T>> f) The asynchronous version of the foreachPartition action, which applies a function f to each partition of this RDD.
com.google.common.base.Optional<String>	getCheckpointFile() Gets the name of the file to which this RDD was checkpointed
StorageLevel	getStorageLevel() Get the RDD's current storage level, or StorageLevel.NONE if none is set.
JavaRDD<java.util.List<T>>	glom() Return an RDD created by coalescing all elements within each partition into an array.
<U> JavaPairRDD<U,Iterable<T>>	groupBy(Function<T,U> f) Return an RDD of grouped elements.
<U> JavaPairRDD<U,Iterable<T>>	groupBy(Function<T,U> f, int numPartitions) Return an RDD of grouped elements.
int	id() A unique ID for this RDD (within its SparkContext).
boolean	isCheckpointed() Return whether this RDD has been checkpointed or not
boolean	isEmpty()
java.util.Iterator<T>	iterator(Partition split, TaskContext taskContext) Internal method to this RDD; will read from cache if applicable, or otherwise compute it.
<U> JavaPairRDD<U,T>	keyBy(Function<T,U> f) Creates tuples of the elements in this RDD by applying f.
<R> JavaRDD<R>	map(Function<T,R> f) Return a new RDD by applying a function to all elements of this RDD.
<U> JavaRDD<U>	mapPartitions(FlatMapFunction<java.util.Iterator<T>,U> f) Return a new RDD by applying a function to each partition of this RDD.
<U> JavaRDD<U>	mapPartitions(FlatMapFunction<java.util.Iterator<T>,U> f, boolean preservesPartitioning) Return a new RDD by applying a function to each partition of this RDD.
JavaDoubleRDD	mapPartitionsToDouble(DoubleFlatMapFunction<java.util.Iterator<T>> f) Return a new RDD by applying a function to each partition of this RDD.
JavaDoubleRDD	mapPartitionsToDouble(DoubleFlatMapFunction<java.util.Iterator<T>> f, boolean preservesPartitioning) Return a new RDD by applying a function to each partition of this RDD.
<K2,V2> JavaPairRDD<K2,V2>	mapPartitionsToPair(PairFlatMapFunction<java.util.Iterator<T>,K2,V2> f) Return a new RDD by applying a function to each partition of this RDD.
<K2,V2> JavaPairRDD<K2,V2>	mapPartitionsToPair(PairFlatMapFunction<java.util.Iterator<T>,K2,V2> f, boolean preservesPartitioning) Return a new RDD by applying a function to each partition of this RDD.
<R> JavaRDD<R>	mapPartitionsWithIndex(Function2<Integer,java.util.Iterator<T>,java.util.Iterator<R>> f, boolean preservesPartitioning) Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.
<R> JavaDoubleRDD	mapToDouble(DoubleFunction<T> f) Return a new RDD by applying a function to all elements of this RDD.
<K2,V2> JavaPairRDD<K2,V2>	mapToPair(PairFunction<T,K2,V2> f) Return a new RDD by applying a function to all elements of this RDD.
T	max(java.util.Comparator<T> comp) Returns the maximum element from this RDD as defined by the specified Comparator[T].
T	min(java.util.Comparator<T> comp) Returns the minimum element from this RDD as defined by the specified Comparator[T].
String	name()
java.util.List<Partition>	partitions() Set of partitions in this RDD.
JavaRDD<String>	pipe(java.util.List<String> command) Return an RDD created by piping elements to a forked external process.
JavaRDD<String>	pipe(java.util.List<String> command, java.util.Map<String,String> env) Return an RDD created by piping elements to a forked external process.
JavaRDD<String>	pipe(String command) Return an RDD created by piping elements to a forked external process.
RDD<T>	rdd()
T	reduce(Function2<T,T,T> f) Reduces the elements of this RDD using the specified commutative and associative binary operator.
void	saveAsObjectFile(String path) Save this RDD as a SequenceFile of serialized objects.
void	saveAsTextFile(String path) Save this RDD as a text file, using string representations of elements.
void	saveAsTextFile(String path, Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec) Save this RDD as a compressed text file, using string representations of elements.
java.util.List<Partition>	splits()
java.util.List<T>	take(int num) Take the first num elements of the RDD.
JavaFutureAction<java.util.List<T>>	takeAsync(int num) The asynchronous version of the take action, which returns a future for retrieving the first num elements of this RDD.
java.util.List<T>	takeOrdered(int num) Returns the first k (smallest) elements from this RDD using the natural ordering for T while maintain the order.
java.util.List<T>	takeOrdered(int num, java.util.Comparator<T> comp) Returns the first k (smallest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.
java.util.List<T>	takeSample(boolean withReplacement, int num)
java.util.List<T>	takeSample(boolean withReplacement, int num, long seed)
java.util.List<T>	toArray() Deprecated. As of Spark 1.0.0, toArray() is deprecated, use collect() instead
String	toDebugString() A description of this RDD and its recursive dependencies for debugging.
java.util.Iterator<T>	toLocalIterator() Return an iterator that contains all of the elements in this RDD.
java.util.List<T>	top(int num) Returns the top k (largest) elements from this RDD using the natural ordering for T.
java.util.List<T>	top(int num, java.util.Comparator<T> comp) Returns the top k (largest) elements from this RDD as defined by the specified Comparator[T].
<U> U	treeAggregate(U zeroValue, Function2<U,T,U> seqOp, Function2<U,U,U> combOp) treeAggregate(U, org.apache.spark.api.java.function.Function2, org.apache.spark.api.java.function.Function2, int) with suggested depth 2.
<U> U	treeAggregate(U zeroValue, Function2<U,T,U> seqOp, Function2<U,U,U> combOp, int depth) Aggregates the elements of this RDD in a multi-level tree pattern.
T	treeReduce(Function2<T,T,T> f) treeReduce(org.apache.spark.api.java.function.Function2, int) with suggested depth 2.
T	treeReduce(Function2<T,T,T> f, int depth) Reduces the elements of this RDD in a multi-level tree pattern.
This	wrapRDD(RDD<T> rdd)
<U> JavaPairRDD<T,U>	zip(JavaRDDLike<U,?> other) Zips this RDD with another one, returning key-value pairs with the first element in each RDD, second element in each RDD, etc.
<U,V> JavaRDD<V>	zipPartitions(JavaRDDLike<U,?> other, FlatMapFunction2<java.util.Iterator<T>,java.util.Iterator<U>,V> f) Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by applying a function to the zipped partitions.
JavaPairRDD<T,Long>	zipWithIndex() Zips this RDD with its element indices.
JavaPairRDD<T,Long>	zipWithUniqueId() Zips this RDD with generated unique Long ids.

Method Detail

wrapRDD

This wrapRDD(RDD<T> rdd)

classTag

scala.reflect.ClassTag<T> classTag()

rdd

RDD<T> rdd()

splits

java.util.List<Partition> splits()

partitions

java.util.List<Partition> partitions()

Set of partitions in this RDD.

context

SparkContext context()

The SparkContext that this RDD was created on.

id

int id()

A unique ID for this RDD (within its SparkContext).

getStorageLevel

StorageLevel getStorageLevel()

Get the RDD's current storage level, or StorageLevel.NONE if none is set.

iterator

java.util.Iterator<T> iterator(Partition split,
                               TaskContext taskContext)

Internal method to this RDD; will read from cache if applicable, or otherwise compute it. This should ''not'' be called by users directly, but is available for implementors of custom subclasses of RDD.

Parameters:

split - (undocumented)

taskContext - (undocumented)

Returns:

(undocumented)

map

<R> JavaRDD<R> map(Function<T,R> f)

Return a new RDD by applying a function to all elements of this RDD.

Parameters:

f - (undocumented)

Returns:

(undocumented)

mapPartitionsWithIndex

<R> JavaRDD<R> mapPartitionsWithIndex(Function2<Integer,java.util.Iterator<T>,java.util.Iterator<R>> f,
                                      boolean preservesPartitioning)

Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.

Parameters:

f - (undocumented)

preservesPartitioning - (undocumented)

Returns:

(undocumented)

mapToDouble

<R> JavaDoubleRDD mapToDouble(DoubleFunction<T> f)

Return a new RDD by applying a function to all elements of this RDD.

Parameters:

f - (undocumented)

Returns:

(undocumented)

mapToPair

<K2,V2> JavaPairRDD<K2,V2> mapToPair(PairFunction<T,K2,V2> f)

Return a new RDD by applying a function to all elements of this RDD.

Parameters:

f - (undocumented)

Returns:

(undocumented)

flatMap

<U> JavaRDD<U> flatMap(FlatMapFunction<T,U> f)

Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

Parameters:

f - (undocumented)

Returns:

(undocumented)

flatMapToDouble

JavaDoubleRDD flatMapToDouble(DoubleFlatMapFunction<T> f)

Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

Parameters:

f - (undocumented)

Returns:

(undocumented)

flatMapToPair

<K2,V2> JavaPairRDD<K2,V2> flatMapToPair(PairFlatMapFunction<T,K2,V2> f)

Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.

Parameters:

f - (undocumented)

Returns:

(undocumented)

mapPartitions

<U> JavaRDD<U> mapPartitions(FlatMapFunction<java.util.Iterator<T>,U> f)

Return a new RDD by applying a function to each partition of this RDD.

Parameters:

f - (undocumented)

Returns:

(undocumented)

mapPartitions

<U> JavaRDD<U> mapPartitions(FlatMapFunction<java.util.Iterator<T>,U> f,
                             boolean preservesPartitioning)

Return a new RDD by applying a function to each partition of this RDD.

Parameters:

f - (undocumented)

preservesPartitioning - (undocumented)

Returns:

(undocumented)

mapPartitionsToDouble

JavaDoubleRDD mapPartitionsToDouble(DoubleFlatMapFunction<java.util.Iterator<T>> f)

Return a new RDD by applying a function to each partition of this RDD.

Parameters:

f - (undocumented)

Returns:

(undocumented)

mapPartitionsToPair

<K2,V2> JavaPairRDD<K2,V2> mapPartitionsToPair(PairFlatMapFunction<java.util.Iterator<T>,K2,V2> f)

Return a new RDD by applying a function to each partition of this RDD.

Parameters:

f - (undocumented)

Returns:

(undocumented)

mapPartitionsToDouble

JavaDoubleRDD mapPartitionsToDouble(DoubleFlatMapFunction<java.util.Iterator<T>> f,
                                    boolean preservesPartitioning)

Return a new RDD by applying a function to each partition of this RDD.

Parameters:

f - (undocumented)

preservesPartitioning - (undocumented)

Returns:

(undocumented)

mapPartitionsToPair

<K2,V2> JavaPairRDD<K2,V2> mapPartitionsToPair(PairFlatMapFunction<java.util.Iterator<T>,K2,V2> f,
                                               boolean preservesPartitioning)

Return a new RDD by applying a function to each partition of this RDD.

Parameters:

f - (undocumented)

preservesPartitioning - (undocumented)

Returns:

(undocumented)

foreachPartition

void foreachPartition(VoidFunction<java.util.Iterator<T>> f)

Applies a function f to each partition of this RDD.

Parameters:

f - (undocumented)

glom

JavaRDD<java.util.List<T>> glom()

Return an RDD created by coalescing all elements within each partition into an array.

Returns:

(undocumented)

cartesian

<U> JavaPairRDD<T,U> cartesian(JavaRDDLike<U,?> other)

Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of elements (a, b) where a is in this and b is in other.

Parameters:

other - (undocumented)

Returns:

(undocumented)

groupBy

<U> JavaPairRDD<U,Iterable<T>> groupBy(Function<T,U> f)

Return an RDD of grouped elements. Each group consists of a key and a sequence of elements mapping to that key.

Parameters:

f - (undocumented)

Returns:

(undocumented)

groupBy

<U> JavaPairRDD<U,Iterable<T>> groupBy(Function<T,U> f,
                                       int numPartitions)

Return an RDD of grouped elements. Each group consists of a key and a sequence of elements mapping to that key.

Parameters:

f - (undocumented)

numPartitions - (undocumented)

Returns:

(undocumented)

pipe

JavaRDD<String> pipe(String command)

Return an RDD created by piping elements to a forked external process.

Parameters:

command - (undocumented)

Returns:

(undocumented)

pipe

JavaRDD<String> pipe(java.util.List<String> command)

Return an RDD created by piping elements to a forked external process.

Parameters:

command - (undocumented)

Returns:

(undocumented)

pipe

JavaRDD<String> pipe(java.util.List<String> command,
                     java.util.Map<String,String> env)

Return an RDD created by piping elements to a forked external process.

Parameters:

command - (undocumented)

env - (undocumented)

Returns:

(undocumented)

zip

<U> JavaPairRDD<T,U> zip(JavaRDDLike<U,?> other)

Zips this RDD with another one, returning key-value pairs with the first element in each RDD, second element in each RDD, etc. Assumes that the two RDDs have the *same number of partitions* and the *same number of elements in each partition* (e.g. one was made through a map on the other).

Parameters:

other - (undocumented)

Returns:

(undocumented)

zipPartitions

<U,V> JavaRDD<V> zipPartitions(JavaRDDLike<U,?> other,
                               FlatMapFunction2<java.util.Iterator<T>,java.util.Iterator<U>,V> f)

Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by applying a function to the zipped partitions. Assumes that all the RDDs have the *same number of partitions*, but does *not* require them to have the same number of elements in each partition.

Parameters:

other - (undocumented)

f - (undocumented)

Returns:

(undocumented)

zipWithUniqueId

JavaPairRDD<T,Long> zipWithUniqueId()

Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k, 2*n+k, ..., where n is the number of partitions. So there may exist gaps, but this method won't trigger a spark job, which is different from RDD.zipWithIndex().

Returns:

(undocumented)

zipWithIndex

JavaPairRDD<T,Long> zipWithIndex()

Zips this RDD with its element indices. The ordering is first based on the partition index and then the ordering of items within each partition. So the first item in the first partition gets index 0, and the last item in the last partition receives the largest index. This is similar to Scala's zipWithIndex but it uses Long instead of Int as the index type. This method needs to trigger a spark job when this RDD contains more than one partitions.

Returns:

(undocumented)

foreach

void foreach(VoidFunction<T> f)

Applies a function f to all elements of this RDD.

Parameters:

f - (undocumented)

collect

java.util.List<T> collect()

Return an array that contains all of the elements in this RDD.

Returns:

(undocumented)

toLocalIterator

java.util.Iterator<T> toLocalIterator()

Return an iterator that contains all of the elements in this RDD.

The iterator will consume as much memory as the largest partition in this RDD.

Returns:

(undocumented)

toArray

java.util.List<T> toArray()

Deprecated. As of Spark 1.0.0, toArray() is deprecated, use collect() instead

Return an array that contains all of the elements in this RDD.

Returns:

(undocumented)

collectPartitions

java.util.List<T>[] collectPartitions(int[] partitionIds)

Return an array that contains all of the elements in a specific partition of this RDD.

Parameters:

partitionIds - (undocumented)

Returns:

(undocumented)

reduce

T reduce(Function2<T,T,T> f)

Reduces the elements of this RDD using the specified commutative and associative binary operator.

Parameters:

f - (undocumented)

Returns:

(undocumented)

treeReduce

T treeReduce(Function2<T,T,T> f,
             int depth)

Reduces the elements of this RDD in a multi-level tree pattern.

Parameters:

depth - suggested depth of the tree

f - (undocumented)

Returns:

(undocumented)

See Also:

reduce(org.apache.spark.api.java.function.Function2)

treeReduce

T treeReduce(Function2<T,T,T> f)

treeReduce(org.apache.spark.api.java.function.Function2, int) with suggested depth 2.

Parameters:

f - (undocumented)

Returns:

(undocumented)

fold

T fold(T zeroValue,
       Function2<T,T,T> f)

Aggregate the elements of each partition, and then the results for all the partitions, using a given associative and commutative function and a neutral "zero value". The function op(t1, t2) is allowed to modify t1 and return it as its result value to avoid object allocation; however, it should not modify t2.

This behaves somewhat differently from fold operations implemented for non-distributed collections in functional languages like Scala. This fold operation may be applied to partitions individually, and then fold those results into the final result, rather than apply the fold to each element sequentially in some defined ordering. For functions that are not commutative, the result may differ from that of a fold applied to a non-distributed collection.

Parameters:

zeroValue - (undocumented)

f - (undocumented)

Returns:

(undocumented)

aggregate

<U> U aggregate(U zeroValue,
                Function2<U,T,U> seqOp,
                Function2<U,U,U> combOp)

Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of this RDD, T. Thus, we need one operation for merging a T into an U and one operation for merging two U's, as in scala.TraversableOnce. Both of these functions are allowed to modify and return their first argument instead of creating a new U to avoid memory allocation.

Parameters:

zeroValue - (undocumented)

seqOp - (undocumented)

combOp - (undocumented)

Returns:

(undocumented)

treeAggregate

<U> U treeAggregate(U zeroValue,
                    Function2<U,T,U> seqOp,
                    Function2<U,U,U> combOp,
                    int depth)

Aggregates the elements of this RDD in a multi-level tree pattern.

Parameters:

depth - suggested depth of the tree

zeroValue - (undocumented)

seqOp - (undocumented)

combOp - (undocumented)

Returns:

(undocumented)

See Also:

aggregate(U, org.apache.spark.api.java.function.Function2, org.apache.spark.api.java.function.Function2)

treeAggregate

<U> U treeAggregate(U zeroValue,
                    Function2<U,T,U> seqOp,
                    Function2<U,U,U> combOp)

treeAggregate(U, org.apache.spark.api.java.function.Function2, org.apache.spark.api.java.function.Function2, int) with suggested depth 2.

Parameters:

zeroValue - (undocumented)

seqOp - (undocumented)

combOp - (undocumented)

Returns:

(undocumented)

count

long count()

Return the number of elements in the RDD.

Returns:

(undocumented)

countApprox

PartialResult<BoundedDouble> countApprox(long timeout,
                                         double confidence)

:: Experimental :: Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.

Parameters:

timeout - (undocumented)

confidence - (undocumented)

Returns:

(undocumented)

countApprox

PartialResult<BoundedDouble> countApprox(long timeout)

:: Experimental :: Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.

Parameters:

timeout - (undocumented)

Returns:

(undocumented)

countByValue

java.util.Map<T,Long> countByValue()

Return the count of each unique value in this RDD as a map of (value, count) pairs. The final combine step happens locally on the master, equivalent to running a single reduce task.

Returns:

(undocumented)

countByValueApprox

PartialResult<java.util.Map<T,BoundedDouble>> countByValueApprox(long timeout,
                                                                 double confidence)

(Experimental) Approximate version of countByValue().

Parameters:

timeout - (undocumented)

confidence - (undocumented)

Returns:

(undocumented)

countByValueApprox

PartialResult<java.util.Map<T,BoundedDouble>> countByValueApprox(long timeout)

(Experimental) Approximate version of countByValue().

Parameters:

timeout - (undocumented)

Returns:

(undocumented)

take

java.util.List<T> take(int num)

Take the first num elements of the RDD. This currently scans the partitions *one by one*, so it will be slow if a lot of partitions are required. In that case, use collect() to get the whole RDD instead.

Parameters:

num - (undocumented)

Returns:

(undocumented)

takeSample

java.util.List<T> takeSample(boolean withReplacement,
                             int num)

takeSample

java.util.List<T> takeSample(boolean withReplacement,
                             int num,
                             long seed)

first

T first()

Return the first element in this RDD.

Returns:

(undocumented)

isEmpty

boolean isEmpty()

Returns:

true if and only if the RDD contains no elements at all. Note that an RDD may be empty even when it has at least 1 partition.

saveAsTextFile

void saveAsTextFile(String path)

Save this RDD as a text file, using string representations of elements.

Parameters:

path - (undocumented)

saveAsTextFile

void saveAsTextFile(String path,
                    Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec)

Save this RDD as a compressed text file, using string representations of elements.

Parameters:

path - (undocumented)

codec - (undocumented)

saveAsObjectFile

void saveAsObjectFile(String path)

Save this RDD as a SequenceFile of serialized objects.

Parameters:

path - (undocumented)

keyBy

<U> JavaPairRDD<U,T> keyBy(Function<T,U> f)

Creates tuples of the elements in this RDD by applying f.

Parameters:

f - (undocumented)

Returns:

(undocumented)

checkpoint

void checkpoint()

Mark this RDD for checkpointing. It will be saved to a file inside the checkpoint directory set with SparkContext.setCheckpointDir() and all references to its parent RDDs will be removed. This function must be called before any job has been executed on this RDD. It is strongly recommended that this RDD is persisted in memory, otherwise saving it on a file will require recomputation.

isCheckpointed

boolean isCheckpointed()

Return whether this RDD has been checkpointed or not

Returns:

(undocumented)

getCheckpointFile

com.google.common.base.Optional<String> getCheckpointFile()

Gets the name of the file to which this RDD was checkpointed

Returns:

(undocumented)

toDebugString

String toDebugString()

A description of this RDD and its recursive dependencies for debugging.

top

java.util.List<T> top(int num,
                      java.util.Comparator<T> comp)

Returns the top k (largest) elements from this RDD as defined by the specified Comparator[T].

Parameters:

num - k, the number of top elements to return

comp - the comparator that defines the order

Returns:

an array of top elements

top

java.util.List<T> top(int num)

Returns the top k (largest) elements from this RDD using the natural ordering for T.

Parameters:

num - k, the number of top elements to return

Returns:

an array of top elements

takeOrdered

java.util.List<T> takeOrdered(int num,
                              java.util.Comparator<T> comp)

Returns the first k (smallest) elements from this RDD as defined by the specified Comparator[T] and maintains the order.

Parameters:

num - k, the number of elements to return

comp - the comparator that defines the order

Returns:

an array of top elements

max

T max(java.util.Comparator<T> comp)

Returns the maximum element from this RDD as defined by the specified Comparator[T].

Parameters:

comp - the comparator that defines ordering

Returns:

the maximum of the RDD

min

T min(java.util.Comparator<T> comp)

Returns the minimum element from this RDD as defined by the specified Comparator[T].

Parameters:

comp - the comparator that defines ordering

Returns:

the minimum of the RDD

takeOrdered

java.util.List<T> takeOrdered(int num)

Returns the first k (smallest) elements from this RDD using the natural ordering for T while maintain the order.

Parameters:

num - k, the number of top elements to return

Returns:

an array of top elements

countApproxDistinct

long countApproxDistinct(double relativeSD)

Return approximate number of distinct elements in the RDD.

The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available here.

Parameters:

relativeSD - Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.

Returns:

(undocumented)

name

String name()

countAsync

JavaFutureAction<Long> countAsync()

The asynchronous version of count, which returns a future for counting the number of elements in this RDD.

Returns:

(undocumented)

collectAsync

JavaFutureAction<java.util.List<T>> collectAsync()

The asynchronous version of collect, which returns a future for retrieving an array containing all of the elements in this RDD.

Returns:

(undocumented)

takeAsync

JavaFutureAction<java.util.List<T>> takeAsync(int num)

The asynchronous version of the take action, which returns a future for retrieving the first num elements of this RDD.

Parameters:

num - (undocumented)

Returns:

(undocumented)

foreachAsync

JavaFutureAction<Void> foreachAsync(VoidFunction<T> f)

The asynchronous version of the foreach action, which applies a function f to all the elements of this RDD.

Parameters:

f - (undocumented)

Returns:

(undocumented)

foreachPartitionAsync

JavaFutureAction<Void> foreachPartitionAsync(VoidFunction<java.util.Iterator<T>> f)

The asynchronous version of the foreachPartition action, which applies a function f to each partition of this RDD.

Parameters:

f - (undocumented)

Returns:

(undocumented)